import requests
import datetime
import pandas as pd
import pymysql
from sklearn.preprocessing import MinMaxScaler
from joblib import dump

class WeatherUtils(object):
    """天气类

    Args:
        object (_type_): _description_
    """

    def __init__(self):
        self.date_list = [
            '2024-07-01',
            '2024-08-01',
            '2024-09-01',
            '2024-10-01',
            '2024-11-01',
            '2024-12-01',
        ]
        self.url = 'http://v1.yiketianqi.com/api'

    def get_data(self):
        """获取天气数据
        """
        
        data_list = []
        for d in self.date_list:
            conf = {
                'appid': '88249599', # 使用自己注册的appid
                'appsecret': 'BA7zIjrM',
                'version': 'history',
                'year': d[:4],
                'month': d[5:7],
                'city': '南昌',
            }
            # 发起请求获取数据
            res = requests.get(self.url + '?', params=conf)
            res_data = res.json()
            # print(res_data)
            # break
            
            for i in res_data['data']:
                data_list.append({
                'date': datetime.datetime.strptime(i['ymd'], '%Y-%m-%d'),
                'bWendu': i['bWendu'],
                'yWendu': i['yWendu'],
                'tianqi': i['tianqi'],
                'fengli': i['fengli'],
            })
        df = pd.DataFrame(data_list)
        df.to_csv('./timing/weather.csv')    
                
class MysqlUtils(object):
    def __init__(self):
        self.conn = pymysql.connect(
            host='localhost',
            user='root',
            password='123456',
            database='scenic',
            port=3306,
            charset='utf8mb4' 
        )
        self.weather_data = pd.read_csv('./timing/weather.csv')
        
    def is_holiday(self, date):
        """是否节假日判断
        """
        if date in ['2024-09-03', '2024-10-01','2024-10-01','2024-10-02','2024-10-03','2024-10-04','2024-10-05','2024-10-06','2024-10-07', '2025-01-01','2025-01-02','2025-01-03']:
            return 1
        return 0            


    def get_data(self):
        cursor = self.conn.cursor(cursor=pymysql.cursors.DictCursor)
        sql="""
        SELECT DATE(g.create_time) as date, HOUR(g.create_time) as hour, count(*) as count FROM order_user_gate_rel g WHERE HOUR(g.create_time) BETWEEN 6 and 23 GROUP BY date, hour
        """
        cursor.execute(sql)
        ret = cursor.fetchall()
        df = pd.DataFrame(ret)
        
        # 合并天气数据
        self.weather_data['date'] = pd.to_datetime(self.weather_data['date'])
        df['date'] = pd.to_datetime(df['date'])
        df_pivot = pd.merge(self.weather_data, df, on='date')
        print(df_pivot.head())
        df_pivot.set_index('date', inplace=True)

        df_pivot['dow'] = df_pivot.index.dayofweek # 星期几（0-6）
        df_pivot['month'] = df_pivot.index.month # 月份
        df_pivot['is_holiday'] = df_pivot.index.map(self.is_holiday)
        # print(df_pivot.head())
        
        # 对星期几和月份进行独热编码
        df_pivot = pd.get_dummies(df_pivot, columns=['dow', 'month', 'tianqi', 'fengli'], dtype=int)
        # 对温度进行类型转换
        df_pivot['bWendu'] = df_pivot['bWendu'].str.replace('°', '').astype(int)
        df_pivot['yWendu'] = df_pivot['yWendu'].str.replace('°', '').astype(int)

        # 归一化入园数
        scaler = MinMaxScaler()
        features = df_pivot[['count']]
        df_pivot['count'] = scaler.fit_transform(features)

        # 归一化天气
        weather_features = df_pivot[['bWendu', 'yWendu']]
        dump(scaler, 'timing/scaler.joblib')
        df_pivot[['bWendu', 'yWendu']] = scaler.fit_transform(weather_features)
        dump(weather_features, 'timing/weather_features.joblib')

        df_pivot.to_csv('timing/scenic_data.csv', index=False)
        

if __name__ == '__main__':
    # wu = WeatherUtils()
    # wu.get_data()
    mu = MysqlUtils()
    mu.get_data()
        